BACKGROUND AND PURPOSE: The percentage signal recovery in non-leakage-corrected (no preload, high flip angle, intermediate TE) DSC-MR imaging is known to differ significantly for glioblastoma, metastasis, and primary CNS lymphoma. Because the percentage signal recovery is influenced by preload and pulse sequence parameters, we investigated whether the percentage signal recovery can still differentiate these common contrast-enhancing neoplasms using a DSC-MR imaging protocol designed for relative CBV accuracy (preload, intermediate flip angle, low TE). MATERIALS AND METHODS: We retrospectively analyzed DSC-MR imaging of treatment-naïve, pathology-proved glioblastomas (n ϭ 14), primary central nervous system lymphomas (n ϭ 7), metastases (n ϭ 20), and meningiomas (n ϭ 13) using a protocol designed for relative CBV accuracy (a one-quarter-dose preload and single-dose bolus of gadobutrol, TR/TE ϭ 1290/40 ms, flip angle ϭ 60°at 1.5T). Mean percentage signal recovery, relative CBV, and normalized baseline signal intensity were compared within contrast-enhancing lesion volumes. Classification accuracy was determined by receiver operating characteristic analysis. RESULTS: Relative CBV best differentiated meningioma from glioblastoma and from metastasis with areas under the curve of 0.84 and 0.82, respectively. The percentage signal recovery best differentiated primary central nervous system lymphoma from metastasis with an area under the curve of 0.81. Relative CBV and percentage signal recovery were similar in differentiating primary central nervous system lymphoma from glioblastoma and from meningioma. Although neither relative CBV nor percentage signal recovery differentiated glioblastoma from metastasis, mean normalized baseline signal intensity achieved 86% sensitivity and 50% specificity. CONCLUSIONS: Similar to results for non-preload-based DSC-MR imaging, percentage signal recovery for one-quarter-dose preloadbased, intermediate flip angle DSC-MR imaging differentiates most pair-wise comparisons of glioblastoma, metastasis, primary central nervous system lymphoma, and meningioma, except for glioblastoma versus metastasis. Differences in normalized post-preload baseline signal for glioblastoma and metastasis, reflecting a snapshot of dynamic contrast enhancement, may motivate the use of single-dose multiecho protocols permitting simultaneous quantification of DSC-MR imaging and dynamic contrast-enhanced MR imaging parameters. ABBREVIATIONS: DCE ϭ dynamic contrast-enhanced; FA ϭ flip angle; NAWM ϭ normal-appearing white matter; PCNSL ϭ primary central nervous system lymphoma; PSR ϭ percentage signal recovery; rCBV ϭ relative cerebral blood volume; SI ϭ signal intensity; AUC ϭ area under the curve C onventional MR imaging cannot always differentiate contrast-enhancing malignant brain tumors, including glioblastoma, primary central nervous system lymphoma (PCNSL), and cerebral metastasis. 1 Meningioma and dural-based metastasis may also appear similar on conventional MR imaging. Because

Purpose Both prolactinomas and nonfunctioning adenomas (NFAs) can present with hyperprolactinemia. Distinguishing them is critical because prolactinomas are effectively managed with dopamine agonists, whereas compressive NFAs are treated surgically. Current guidelines rely only on serum prolactin (PRL) levels, which are neither sensitive nor specific enough. Recent studies suggest that accounting for tumor volume may improve diagnosis. The objective of this study is to investigate the diagnostic utility of PRL, tumor volume, and imaging features in differentiating prolactinoma and NFA. Methods Adult patients with pathologically confirmed prolactinoma (n = 21) or NFA with hyperprolactinemia (n = 58) between 2013 and 2020 were retrospectively identified. Diagnostic performance of clinical and imaging variables was analyzed using receiver-operating characteristic curves to calculate area under the curve (AUC). Results Tumor volume and PRL positively correlated for prolactinoma (r = 0.4839, p = 0.0263) but not for NFA (r = 0.0421, p = 0.7536). PRL distinguished prolactinomas from NFAs with an AUC of 0.8892 (p < 0.0001) and optimal cut-off value of 62.45 ng/ml, yielding a sensitivity of 85.71% and specificity of 94.83%. The ratio of PRL to tumor volume had an AUC of 0.9647 (p < 0.0001) and optimal cut-off value of 21.62 (ng/ml)/cm3 with sensitivity of 100% and specificity of 82.76%. Binary logistic regression found that PRL was a significant positive predictor of prolactinoma diagnosis, whereas tumor volume, presence of CSI not previously defined, and T2 hyperintensity were significant negative predictors. The regression model had an AUC of 0.9915 (p < 0.0001). Conclusions Consideration of tumor volume improves differentiation between prolactinomas and NFAs, which in turn leads to effective management.

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